With the onset of COVID-19, interest in the how well, or rather unwell, small business are doing has increased considerably. At any point in time, the small business economy is a mixed bag of businesses, some just starting out and enjoying expansion, some stagnating, and some facing decline. Under unfavourable economic circumstances, such as those created by the pandemic, more businesses are likely to experience hardships and become financially distressed.
In this report we approach distress from a cash flow point of view. After all, cash flow is the ‘life blood’ of business: when it flows adequately the business operates, when it experiences disruptions, the business undergoes distress, which may even eventually lead to failure.
A simple model of operating cash flows
According to this simplified view things can go wrong when cash in-flows decrease/slow down or cash out-flows increase/speed. In this current study, we look at the following particular aspects of these potential cash flow problems:
One critical contributing factor to financial distress in any business is the lack of cash from sales to cover ongoing expenses, pay creditors in time and eventually secure a margin in operating activities.
In order to understand how many small businesses struggle with decreasing sales, we look at the proportion of businesses that experienced a major drop (> 20%) in their recorded sales over a period1. Sales can be highly seasonal, so we use a moving 12-month sales window as the baseline. Also, rather than looking at drops over a month, we look at quarterly changes, which removes the effect of shorter terms shocks (a drop followed by a bounce-back).
In all three depicted economies, the proportion of businesses experiencing a major drop in invoiced value seems to have been fairly stable historically and it is comparable in terms of order of magnitude. The proportion ranges from around 10-12%. It is reasonable to assume that this is natural rate, a baseline so to say. The small business population is ever-changing and at any point in time it will always include businesses that experience substantial hardship even during a generally favourable economic climate.
Quite unsurprisingly, 2020 stirred things up and it is reflected in the major drop in sales metric. In all the countries it climbed to an unprecedented peak. Although in the past, the UK had consistently ranked the lowest on the metric, it climbed to the highest level among the three countries peaking at more than 17% in June and July 2020.
Other than being able to keep customers and make sales, it is also important for a business to collect the cash from those sales in a timely manner so that their cash flow remains smooth and stable. In hard times, some businesses may experience increasing days-to-payment (the difference between the date if the credit sale and the eventual date of full payment) as some of their customers may settle their account later than usual.
The time series below shows the proportion of small businesses that saw a major increase (>10%) in the median payment times of the credit invoices they issued in a particular month. For the purposes of this metric, we cap invoice payment terms at 60 days. In other words, for each business we only consider invoices that were meant to be paid until the end of the subsequent month 2.
Compared to what we have seen historically in terms of businesses affected by increased payment times in any given month, the recent few months show signs of a slightly bigger proportion of businesses being exposed to such problems, but there has not been a drastic change.
On the aggregate, the average days-to-payment times3 have remained fairly stable over time with some seasonal variation. That pattern was somewhat disrupted in recent months. In New Zealand and the United Kingdom the metric first dipped in April 2020 and then started a slow climb. The counterintuitive dip may be partially due to the fact that potentially risky invoices may have never been raised and those that were actually raised were the ones that were reasonably expected to get paid even during hard times.
A shock can impact both sides of the cash flow. Drops in cash in (~revenue from sales) is likely to affect many businesses. That may be made worse by no change on the cash-out side, that is when the business cannot scale back operations and the same level of expenditure will arise. The next graphs take a look at how many businesses are affected ‘symmetrically’ and how many suffer just a one-sided shock.
Cash Burn Rate measures the rate at which a business would deplete its cash pool in a loss-generating scenario. In other words, how long could the business survive in the absence of any revenue but having to cover the usual recurring expenses. More specifically, it is calculated by dividing all available cash by the sum of operating expenses such as rent, salaries, and other overhead:
\[\sf\text{Cash Burn Rate} = \frac{\text{Cash}}{\text{Monthly Operating Expenses}}\]
Because further refinements are needed in how the components of this metric are derived from Xero data at scale, a cruder version is calculated. Instead of operating expenses we use the broader category of all expenses, which likely tilts the metric toward indicating a more pessimistic picture. The figures below show the distribution of businesses on their value of Cash Burn Rate in the three countries over a period of 6 years (overlayed on each other).
By and large all three countries show similar change patterns over time. The distributions remained quite similar between 2015-2019. In all three countries 2020 is a departure from what we observed for 2015-2019. This seemingly favourable shift to more businesses having better Cash Burn Rates may be partly due to more conservative expenditure and withdrawals by the owners. We have seen from earlier cash flow analysis that many small business owners tend to withdraw what they may see as excess cash. In addition to this increasing caution, some businesses may also benefit from government subsudies coming in as cash, or expenditure temporarily decreasing as a result of tax allowances and deferrals.
For the purpose of this report, we use the end of the month as the cut-off point for recorded sales. While this approach will disregard any invoices that are backdated for any given month (raised on the platform after the month ends), this way month-on-month changes will be comparable even for very recent periods.↩︎
This cuts of the potentially long right tail of the payment term distribution that would likely skew any metric based on all payment terms. At the same time this makes (recent) months comparable as each month would be based on a fixed-size (2-month) time window for the metric. Because of this the most recent month is always dropped from the time series.↩︎
For each business and each month we calculate a median days-to-payment metric for all invoices that did not require immediate payment and were due by the end of the consecutive month. This metric in payment term agnostic as it captures all termed invoices due within that time frame rather than just singling out one prominent payment term. Given that in all three economies invoices would predominantly be termed up to 30 days maximum, this time window includes most invoices.↩︎